Parallel processing techniques for expediting reconciliation for a hierarchy of forecasts on a computer system

ABSTRACT

A parallel processing technique can be used to expedite reconciliation of a hierarchy of forecasts on a computer system. As one example, the computer system can receive forecasts that have a hierarchical relationship with respect to one another. The computer system can distribute the forecasts among a group of computing nodes by time point, so that all data points corresponding to the same time point in the forecasts are assigned to the same computing node. The computing nodes can receive the datasets corresponding to the time points, organize the data points in each of the datasets by forecast to generate ordered datasets, and assign the ordered datasets to processing threads. The processing threads (across the computing nodes) can then execute a reconciliation process in parallel to one another to generate reconciled values, which can be output by the computing nodes.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 63/457,391 filed May 5, 2023, and toU.S. Provisional Patent Application No. 63/461,208, filed Apr. 21, 2023,the entirety of each of which is hereby incorporated by referenceherein.

TECHNICAL FIELD

The present disclosure relates generally to parallel processing in acomputing cluster. More specifically, but not by way of limitation, thisdisclosure relates to a parallel processing technique for expeditingreconciliation of a hierarchy of forecasts on a computer system.

BACKGROUND

In some situations, time series data can be organized hierarchically.For example, an administrator of a network can receive a set of timeseries indicating the total number of packets flowing through thenetwork's servers over a given time window, such as one month. Each timeseries can correspond to an individual server and each data point in thetime series can indicate the total number of packets flowing throughthat server at a particular point in time, such as on a given day. Thedata points can be collected daily or at another frequency. The serverscan be geographically distributed across a country, such as the UnitedStates. The time series may be organized hierarchically by geographicalregion, such as by states, counties, and cities within the country. Whentime series data is organized in a hierarchical fashion, there are oftenconstraints that link the time series together at different levels ofthe hierarchy. For example, the total packet flow through the entirenetwork should be the sum of all of the packet flow through all of thestates covered by the network. While these constraints may be inherentlysatisfied by actual time-series data, it can be more challenging to meetthese constraints in the context of predictive forecasting.

Forecasting can involve generating time-stamped data (e.g., a timeseries) with predicted values over a future time window. Such forecastsare normally generated using models, such as machine-learning models. Insome cases, the forecasts can have a hierarchical relationship withrespect to one another. For example, the forecasts can be generated topredict the packet flow through the network at the state level, thecounty level, and the city level over a future time window. But imposingthe abovementioned constraints during a forecasting process may bechallenging, because the forecasts are often individually generated bythe models, without regard for the other levels of the hierarchy. As aresult, such forecasts often do not respect the constraints. To resolvethis problem, after the forecasts have been generated, a reconciliationprocess may be applied to the forecasts to adjust the forecasts so thatthey adhere to those constraints.

SUMMARY

One example of the present disclosure includes a system comprising oneor more processors and one or more memories. The one or more memoriescan include program code that is executable by the one or moreprocessors for causing the one or more processors to perform operations.The operations can include receiving a plurality of forecasts that havea hierarchical relationship with respect to one another, wherein eachforecast among the plurality of forecasts corresponds to a respectivelevel of a hierarchy, and wherein at least one forecast in the pluralityof forecasts corresponds to a higher level of the hierarchy than atleast one other forecast of the plurality of forecasts. The operationscan include distributing the plurality of forecasts among a plurality ofcomputing nodes of a distributed computing environment by time point,such that all data points corresponding to a same time point in theplurality of forecasts are assigned to a same computing node of theplurality of computing nodes. The plurality of computing nodes can beconfigured to collectively process the plurality of forecasts inparallel to implement a reconciliation process that involves adjustingthe plurality of forecasts subject to an aggregation constraint. Theplurality of computing nodes can be further configured to: receive aplurality of datasets corresponding to a plurality of time points, eachdataset of the plurality of datasets including a respective set of datapoints from the plurality of forecasts corresponding to a single timepoint; organize the respective set of data points in each of theplurality of datasets by forecast to generate a plurality of ordereddatasets; assign the plurality of ordered datasets to a plurality ofprocessing threads on the plurality of computing nodes, the plurality ofprocessing threads being executable in parallel to implement respectiveportions of the reconciliation process using the plurality of ordereddatasets; execute the plurality of processing threads to implement thereconciliation process on the plurality of forecasts, to therebygenerate a plurality of reconciled values; and output the plurality ofreconciled values.

Another example of the present disclosure includes method of operations.The operations can include receiving a plurality of forecasts that havea hierarchical relationship with respect to one another, wherein eachforecast among the plurality of forecasts corresponds to a respectivelevel of a hierarchy, and wherein at least one forecast in the pluralityof forecasts corresponds to a higher level of the hierarchy than atleast one other forecast of the plurality of forecasts. The operationscan include distributing the plurality of forecasts among a plurality ofcomputing nodes of a distributed computing environment by time point,such that all data points corresponding to a same time point in theplurality of forecasts are assigned to a same computing node of theplurality of computing nodes. The plurality of computing nodes can beconfigured to collectively process the plurality of forecasts inparallel to implement a reconciliation process that involves adjustingthe plurality of forecasts subject to an aggregation constraint. Theplurality of computing nodes can be further configured to: receive aplurality of datasets corresponding to a plurality of time points, eachdataset of the plurality of datasets including a respective set of datapoints from the plurality of forecasts corresponding to a single timepoint; organize the respective set of data points in each of theplurality of datasets by forecast to generate a plurality of ordereddatasets; assign the plurality of ordered datasets to a plurality ofprocessing threads on the plurality of computing nodes, the plurality ofprocessing threads being executable in parallel to implement respectiveportions of the reconciliation process using the plurality of ordereddatasets; execute the plurality of processing threads to implement thereconciliation process on the plurality of forecasts, to therebygenerate a plurality of reconciled values; and output the plurality ofreconciled values. The operations can be implemented by one or moreprocessors.

Yet another example of the present disclosure includes a non-transitorycomputer-readable medium comprising program code that is executable byone or more processors for causing the one or more processors to performoperations. The operations can include receiving a plurality offorecasts that have a hierarchical relationship with respect to oneanother, wherein each forecast among the plurality of forecastscorresponds to a respective level of a hierarchy, and wherein at leastone forecast in the plurality of forecasts corresponds to a higher levelof the hierarchy than at least one other forecast of the plurality offorecasts. The operations can include distributing the plurality offorecasts among a plurality of computing nodes of a distributedcomputing environment by time point, such that all data pointscorresponding to a same time point in the plurality of forecasts areassigned to a same computing node of the plurality of computing nodes.The plurality of computing nodes can be configured to collectivelyprocess the plurality of forecasts in parallel to implement areconciliation process that involves adjusting the plurality offorecasts subject to an aggregation constraint. The plurality ofcomputing nodes can be further configured to: receive a plurality ofdatasets corresponding to a plurality of time points, each dataset ofthe plurality of datasets including a respective set of data points fromthe plurality of forecasts corresponding to a single time point;organize the respective set of data points in each of the plurality ofdatasets by forecast to generate a plurality of ordered datasets; assignthe plurality of ordered datasets to a plurality of processing threadson the plurality of computing nodes, the plurality of processing threadsbeing executable in parallel to implement respective portions of thereconciliation process using the plurality of ordered datasets; executethe plurality of processing threads to implement the reconciliationprocess on the plurality of forecasts, to thereby generate a pluralityof reconciled values; and output the plurality of reconciled values.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is described in conjunction with the appendedFIGURES:

FIG. 1 shows an example of the hardware components of a datatransmission network according to some aspects of the presentdisclosure.

FIG. 2 shows an example network including an example set of devicescommunicating with each other over an exchange system according to someaspects of the present disclosure.

FIG. 3 shows an example representation of a conceptual model of acommunications protocol system according to some aspects of the presentdisclosure.

FIG. 4 shows a communications grid computing system including a varietyof control and worker nodes according to some aspects of the presentdisclosure.

FIG. 5 shows a flow chart showing an example process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects of the present disclosure.

FIG. 6 shows a portion of a communications grid computing systemincluding a control node and a worker node according to some aspects ofthe present disclosure.

FIG. 7 shows a flow chart showing an example method 700 for executing aproject within a grid computing system according to some aspects of thepresent disclosure.

FIG. 8 shows a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects of the presentdisclosure.

FIG. 9 shows a flow chart of an example process including operationsperformed by an event stream processing engine according to some aspectsof the present disclosure.

FIG. 10 shows an ESP system interfacing between publishing device andevent subscribing devices according to some aspects of the presentdisclosure.

FIG. 11 shows a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects of the presentdisclosure.

FIG. 12 shows a node-link diagram of an example of a neural networkaccording to some aspects of the present disclosure.

FIG. 13 shows various aspects of the use of containers as a mechanism toallocate processing, storage and/or other resources of a processingsystem to the performance of various analyses according to some aspectsof the present disclosure.

FIG. 14 shows a block diagram of an example of a computer system forexpediting reconciliation of a hierarchy of forecasts according to someaspects of the present disclosure.

FIG. 15 shows a block diagram of an example of a hierarchy of forecastsaccording to some aspects of the present disclosure.

FIG. 16 shows an example of a hierarchy of forecasts according to someaspects of the present disclosure.

FIG. 17 shows an example of a hierarchy of data tables according to someaspects of the present disclosure.

FIG. 18 shows an example of a dataset corresponding to a particular timepoint in a set of forecasts according to some aspects of the presentdisclosure.

FIG. 19 shows an example of an S-matrix according to some aspects of thepresent disclosure.

FIG. 20 shows a block diagram of an example of a computer system with amodified S-matrix and a modified G-matrix according to some aspects ofthe present disclosure.

FIG. 21 shows an example of a dataset, an S-matrix, a modified dataset,and a modified S-matrix according to some aspects of the presentdisclosure.

FIG. 22 shows a flowchart of an example of a process for dividing anddistributing forecast data among a group of nodes according to someaspects of the present disclosure.

FIG. 23 shows a flowchart of an example of a process for performingreconciliation on a computing node according to some aspects of thepresent disclosure.

FIG. 24 shows a flowchart of an example of a process for S-matrixvalidation according to some aspects of the present disclosure.

FIG. 25 shows a flowchart of an example of a process for performingreconciliation on a processing thread of a computing node according tosome aspects of the present disclosure.

In the appended FIGURES, similar components or features can have thesame reference number. Further, various components of the same type maybe distinguished by following the reference number with a lowercaseletter that distinguishes among the similar components. If only thefirst reference number is used in the specification, the description isapplicable to any one of the similar components having the same firstreference number irrespective of the lowercase letter.

DETAILED DESCRIPTION

Computerized forecasting can involve a computer system executing a model(e.g., a machine-learning model) to generate a forecast, which caninclude time-stamped data of predicted values over a future time window.In some cases, the computer system can generate multiple forecasts thathave a hierarchical relationship with respect to one another. Forexample, a first forecast in the hierarchy may be considered a parentand a second forecast in the hierarchy may be considered a child of theparent. Given that the forecasts have a hierarchical relationship withrespect to one another, it may be desirable to impose certainconstraints such as an aggregation constraint on the forecasts. Anaggregation constraint may be a requirement that values at a lower levelof the hierarchy sum up to values at a higher level of the hierarchy.For instance, the total of all packet flow through a state at any givenpoint in time should be the sum of all packet flow through all regionsof the state at that point in time. But imposing such constraints duringa forecasting process may be challenging for a variety of reasons. Tohelp resolve this problem, after the forecasts have been generated, thecomputer system may perform a reconciliation process on the forecasts toadjust the forecasts so that they adhere to those constraints.

Existing reconciliation processes are technically complex. They normallyinvolve computationally intensive matrix operations with highcomputational overhead. As a result, reconciliation can be slow,inefficient, and resource intensive (e.g., it can consume a significantamount of processing power, memory, and storage) when performed inconventional ways on a computer. This can prevent the computer fromperforming other tasks and may introduce latency in forecastingapplications.

Existing reconciliation processes may also only handle two forecasts ofa hierarchy at a time. For example, if the reconciliation process is tobe applied to a four level hierarchy, it may reconcile levels one andtwo, and then levels two and three, and then levels three and four. Thispairwise reconciliation process can be difficult to implement and may becomputationally intensive.

Some examples of the present disclosure can overcome one or more of theabovementioned problems by performing reconciliation on a set ofhierarchical forecasts (e.g., forecasts having a hierarchicalrelationship to one another) using two levels of parallel processing ona computing cluster. The two levels of parallel processing can include afirst level of parallelism in which the computing cluster divides theset of forecasts by time point into datasets. Each dataset maycorrespond to a single time point. For example, each dataset may onlyconsist of the data points related to that single time point in theforecasts. After dividing the forecasts into the datasets by time point,the computing cluster can distribute the datasets among its computingnodes to be processed in parallel by the computing nodes. The two levelsof parallel processing can also include a second level of parallelism.The second level of parallelism can be applied at each individualcomputing node. In particular, each individual computing node canprocess its assigned datasets in parallel using multiple threads(processing threads). For example, if five datasets are assigned to asingle node, those five datasets can be processed on that single node inparallel using five threads, where each dataset is processed by one ofthe threads. This can involve the thread executing a reconciliationprocess on the data points in its assigned dataset. Using thesetechniques, the reconciliation process can be distributed among multiplenodes of a computing cluster and parallelized in two ways. This cansignificantly expedite the reconciliation process and reduce latency.

The techniques described herein can also perform reconciliationsimultaneously across any number of levels of a hierarchy. For example,by dividing and distributing all levels of the hierarchy across thecomputing nodes for parallel processing, as described above, the systemcan simultaneously reconcile all levels of an N-level hierarchy (e.g.,rather than performing pairwise reconciliation on two levels at a time).This may lead to improved accuracy as compared to conventionalapproaches.

As noted above, an overall reconciliation process for the set offorecasts can be divided up and distributed among multiple threads ofmultiple computing nodes. Each thread can execute a respectivereconciliation process on its assigned dataset to generate one or morereconciled values for the corresponding time point. The computingcluster can then collect the reconciled values for some or all of thetime points and use them to generate reconciled forecasts (e.g.,reconciled versions of the original set of forecasts) that satisfy oneor more predefined constraints.

In some examples, each computing node can inspect one of its assigneddatasets to construct a summing matrix (“S-matrix”) to be used by itsthreads in its reconciliation processes. The S-matrix can encode theaggregation constraints between the levels of the hierarchy. Thecomputing nodes can then compare their S-matrices to one another tocheck whether they match. This check can be performed prior to thecomputing nodes executing their respective reconciliation processes ontheir assigned datasets. If the check succeeds, the computing nodes canproceed to execute their respective reconciliation processes. If thecheck fails, for example because at least two of the nodes havedifferent S-matrices, it may mean that the nodes disagree about theaggregation constraints, so the computing cluster can issue an errornotification. By performing this validation prior to executing thereconciliation processes, the computing cluster can help avoid wastingcomputing resources by performing the reconciliation processes insituations that would yield inaccurate results, because the S-matricesare used in the reconciliation computations.

In some examples, each thread can check whether its assigned dataset hasany missing values. For example, there can be a dataset that is assignedto a thread on a computing node. The dataset can correspond to a timepoint in a set of three forecasts. In the dataset, there can be threedata points—one data point extracted from each of the three forecasts.The thread can analyze the three data points to determine if any of themhave missing values (e.g., an empty or NULL value for a data point). Adata point may have a missing value for any number of reasons, such as aproblem with the forecasting model that produced the correspondingforecast. If the thread determines that a data point is missing a value,the thread can execute a missing-value handling process. This caninvolve dynamically modifying the S-matrix that it previously computed,for example, to remove a row and/or column related to the missing valuefrom the S-matrix. The thread can then use the dynamically modifiedS-matrix to perform its reconciliation process. In this way, each threadcan perform a missing value check on each of its assigned datasets anddynamically adjust the pre-computed S-matrix upon detecting a missingvalue in a dataset, so that the missing value does not negatively affectthe reconciliation process for that dataset.

Because of some or all of the features described above, the techniquesdescribed herein can be more stable and hundreds or thousands of timesfaster than conventional reconciliation approaches. For example,existing reconciliation libraries such as HTS and Fabletools eithercannot perform any parallelization at all or can only performed limitedparallel processing on a single computer, not a cluster of distributednodes. The inability to perform parallel reconciliation or distributedreconciliation significantly limits the speed and abilities of theselibraries. This can result in memory errors and scalability problemswhen applied to larger datasets. Some existing reconciliation libraries,such as existing Python packages, also do not compute an S-matrix ororganize the forecast data. Thus, the techniques described herein canprovide numerous technical improvements over existing reconciliationapproaches.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements but, like the illustrativeexamples, should not be used to limit the present disclosure.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1 , computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10 ), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices 102 may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices 102 may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices 102 may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices 102 directly to computing environment 114or to network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However, in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing or containing data. A machine-readablestorage medium or computer-readable storage medium may include anon-transitory medium in which data can be stored and that does notinclude carrier waves and/or transitory electronic signals. Examples ofa non-transitory medium may include, for example, a magnetic disk ortape, optical storage media such as compact disk or digital versatiledisk, flash memory, memory or memory devices. A computer-program productmay include code and/or machine-executable instructions that mayrepresent a procedure, a function, a subprogram, a program, a routine, asubroutine, a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, and network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more server farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices 102, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1 . Services provided by thecloud network can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, and/orsystems. In some embodiments, the computers, servers, and/or systemsthat make up the cloud network 116 are different from the user's ownon-premises computers, servers, and/or systems. For example, the cloudnetwork 116 may host an application, and a user may, via a communicationnetwork such as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server may includea server stack. As another example, data may be processed as part ofcomputing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between servers 106 and computing environment 114 orbetween a server and a device) may occur over one or more networks 108.Networks 108 may include one or more of a variety of different types ofnetworks, including a wireless network, a wired network, or acombination of a wired and wireless network. Examples of suitablenetworks include the Internet, a personal area network, a local areanetwork (LAN), a wide area network (WAN), or a wireless local areanetwork (WLAN). A wireless network may include a wireless interface orcombination of wireless interfaces. As an example, a network in the oneor more networks 108 may include a short-range communication channel,such as a BLUETOOTH® communication channel or a BLUETOOTH® Low Energycommunication channel. A wired network may include a wired interface.The wired and/or wireless networks may be implemented using routers,access points, bridges, gateways, or the like, to connect devices in thenetwork 108, as will be further described with respect to FIG. 2 . Theone or more networks 108 can be incorporated entirely within or caninclude an intranet, an extranet, or a combination thereof. In oneembodiment, communications between two or more systems and/or devicescan be achieved by a secure communications protocol, such as securesockets layer (SSL) or transport layer security (TLS). In addition, dataand/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. This will be described further below with respectto FIG. 2 .

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2 , network device 204 can transmit a communicationover a network (e.g., a cellular network via a base station). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station. The communication can also be routedto computing environment 214 via base station. For example, networkdevice 204 may collect data either from its surrounding environment orfrom other network devices (such as network devices 205-209) andtransmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting characteristics of their environment.For example, the network devices may include sensors such as watersensors, power sensors, electrical current sensors, chemical sensors,optical sensors, pressure sensors, geographic or position sensors (e.g.,GPS), velocity sensors, acceleration sensors, flow rate sensors, amongothers. Examples of characteristics that may be sensed include force,torque, load, strain, position, temperature, air pressure, fluid flow,chemical properties, resistance, electromagnetic fields, radiation,irradiance, proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems (e.g., an oil drillingoperation). The network devices may detect and record data related tothe environment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc., and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data they collectbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze and/or store data from or pertaining tocommunications, client device operations, client rules, and/oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2 , computing environment 214 may includea machine 240 that is a web server. Thus, computing environment 214 canretrieve data of interest, such as client information (e.g., productinformation, client rules, etc.), technical product details, news,current or predicted weather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2 ) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 301-307. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 301. Physical layer 301represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 301 also defines protocols that may controlcommunications within a data transmission network.

Link layer 302 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer 302 manages node-to-nodecommunications, such as within a grid computing environment. Link layer302 can detect and correct errors (e.g., transmission errors in thephysical layer 301). Link layer 302 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 303 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 303 can also define the processes used to structure localaddressing within the network.

Transport layer 304 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 304 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 304 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 305 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 306 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types and/orencodings known to be accepted by an application or network layer.

Application layer 307 interacts directly with software applications andend users, and manages communications between them. Application layer307 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 321 and 322 are shown to operate inlower levels, such as physical layer 301 and link layer 302,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the link layer, and a router can operate in thenetwork layer. Inter-network connection components 323 and 328 are shownto operate on higher levels, such as layers 303-307. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3 . For example, referringback to FIG. 2 , one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be received or stored by a machine other than a control node (e.g.,a HADOOP® standard-compliant data node employing the HADOOP® DistributedFile System, or HDFS).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, and coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project codes running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local to (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks), then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes. The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, and the port numberon which the primary control node is accepting connections from peernodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, or receivedfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, and information about how to authenticate the node, amongother information. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes in the grid, unique identifiers of the nodes, or theirrelationships with the primary control node) and the status of a project(including, for example, the status of each worker node's portion of theproject). The snapshot may also include analysis or results receivedfrom worker nodes in the communications grid. The backup control nodesmay receive and store the backup data received from the primary controlnode. The backup control nodes may transmit a request for such asnapshot (or other information) from the primary control node, or theprimary control node may send such information periodically to thebackup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process 500 foradjusting a communications grid or a work project in a communicationsgrid after a failure of a node, according to embodiments of the presenttechnology. The process may include, for example, receiving grid statusinformation including a project status of a portion of a project beingexecuted by a node in the communications grid, as described in operation502. For example, a control node (e.g., a backup control node connectedto a primary control node and a worker node on a communications grid)may receive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid computing system 600includes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4 , communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 include multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG. 2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DBMS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS 628. For example, UDF 626 can be invoked by the DBMS 628 toprovide data to the GESC 620 for processing. The UDF 626 may establish asocket connection (not shown) with the GESC 620 to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC 620 by writingdata to shared memory accessible by both the UDF 626 and the GESC 620

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1 . Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DBMS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DBMS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4 , data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method 700 forexecuting a project within a grid computing system, according toembodiments of the present technology. As described with respect to FIG.6 , the GESC at the control node may transmit data with a client device(e.g., client device 630) to receive queries for executing a project andto respond to those queries after large amounts of data have beenprocessed. The query may be transmitted to the control node, where thequery may include a request for executing a project, as described inoperation 702. The query can contain instructions on the type of dataanalysis to be performed in the project and whether the project shouldbe executed using the grid-based computing environment, as shown inoperation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project, asdescribed in operation 712.

As noted with respect to FIG. 2 , the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2 , and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10 , may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2 ) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2 .

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2 . As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2 .The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishingdevice 1022 and event subscribing devices 1024 a-c, according toembodiments of the present technology. ESP system 1000 may include ESPdevice or subsystem 1001, event publishing device 1022, an eventsubscribing device A 1024 a, an event subscribing device B 1024 b, andan event subscribing device C 1024 c. Input event streams are output toESP subsystem 1001 by publishing device 1022. In alternativeembodiments, the input event streams may be created by a plurality ofpublishing devices. The plurality of publishing devices further maypublish event streams to other ESP devices. The one or more continuousqueries instantiated by ESPE 800 may analyze and process the input eventstreams to form output event streams output to event subscribing deviceA 1024 a, event subscribing device B 1024 b, and event subscribingdevice C 1024 c. ESP system 1000 may include a greater or a fewer numberof event subscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a, event subscribing device B 1024 b, and eventsubscribing device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a, event subscribingdevice B 1024 b, and event subscribing device C 1024 c.

Referring back to FIG. 9 , operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2 , data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS)®, SAS Viya® of all which are by SAS Institute Inc. of Cary, NorthCarolina.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11 .

In block 1102, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1104, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1106, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if, at block 1108, the machine-learning model has aninadequate degree of accuracy for a particular task, the process canreturn to block 1104, where the machine-learning model can be furthertrained using additional training data or otherwise modified to improveaccuracy. However, if, at block 1108, the machine-learning model has anadequate degree of accuracy for the particular task, the process cancontinue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12 . The neural network 1200 is representedas multiple layers of neurons 1208 that can exchange data between oneanother via connections 1255 that may be selectively instantiatedthereamong. The layers include an input layer 1202 for receiving inputdata provided at inputs 1222, one or more hidden layers 1204, and anoutput layer 1206 for providing a result at outputs 1277. The hiddenlayer(s) 1204 are referred to as hidden because they may not be directlyobservable or have their inputs or outputs directly accessible duringthe normal functioning of the neural network 1200. Although the neuralnetwork 1200 is shown as having a specific number of layers and neuronsfor exemplary purposes, the neural network 1200 can have any number andcombination of layers, and each layer can have any number andcombination of neurons.

The neurons 1208 and connections 1255 thereamong may have numericweights, which can be tuned during training of the neural network 1200.For example, training data can be provided to at least the inputs 1222to the input layer 1202 of the neural network 1200, and the neuralnetwork 1200 can use the training data to tune one or more numericweights of the neural network 1200. In some examples, the neural network1200 can be trained using backpropagation. Backpropagation can includedetermining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 at theoutputs 1277 and a desired output of the neural network 1200. Based onthe gradient, one or more numeric weights of the neural network 1200 canbe updated to reduce the difference therebetween, thereby increasing theaccuracy of the neural network 1200. This process can be repeatedmultiple times to train the neural network 1200. For example, thisprocess can be repeated hundreds or thousands of times to train theneural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, the connections 1255 areinstantiated and/or weighted so that every neuron 1208 only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron 1208to the next neuron 1208 in a feed-forward neural network. Such a“forward” direction may be defined as proceeding from the input layer1202 through the one or more hidden layers 1204, and toward the outputlayer 1206.

In other examples, the neural network 1200 may be a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops among the connections 1255, thereby allowing data to propagate inboth forward and backward through the neural network 1200. Such a“backward” direction may be defined as proceeding in the oppositedirection of forward, such as from the output layer 1206 through the oneor more hidden layers 1204, and toward the input layer 1202. This canallow for information to persist within the recurrent neural network.For example, a recurrent neural network can determine an output based atleast partially on information that the recurrent neural network hasseen before, giving the recurrent neural network the ability to useprevious input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer(“subsequent” in the sense of moving “forward”) of the neural network1200. Each subsequent layer of the neural network 1200 can repeat thisprocess until the neural network 1200 outputs a final result at theoutputs 1277 of the output layer 1206. For example, the neural network1200 can receive a vector of numbers at the inputs 1222 of the inputlayer 1202. The neural network 1200 can multiply the vector of numbersby a matrix of numeric weights to determine a weighted vector. Thematrix of numeric weights can be tuned during the training of the neuralnetwork 1200. The neural network 1200 can transform the weighted vectorusing a nonlinearity, such as a sigmoid tangent or the hyperbolictangent. In some examples, the nonlinearity can include a rectifiedlinear unit, which can be expressed using the equation y=max(x, 0) wherey is the output and x is an input value from the weighted vector. Thetransformed output can be supplied to a subsequent layer (e.g., a hiddenlayer 1204) of the neural network 1200. The subsequent layer of theneural network 1200 can receive the transformed output, multiply thetransformed output by a matrix of numeric weights and a nonlinearity,and provide the result to yet another layer of the neural network 1200(e.g., another, subsequent, hidden layer 1204). This process continuesuntil the neural network 1200 outputs a final result at the outputs 1277of the output layer 1206.

As also depicted in FIG. 12 , the neural network 1200 may be implementedeither through the execution of the instructions of one or more routines1244 by central processing units (CPUs), or through the use of one ormore neuromorphic devices 1250 that incorporate a set of memristors (orother similar components) that each function to implement one of theneurons 1208 in hardware. Where multiple neuromorphic devices 1250 areused, they may be interconnected in a depth-wise manner to enableimplementing neural networks with greater quantities of layers, and/orin a width-wise manner to enable implementing neural networks havinggreater quantities of neurons 1208 per layer.

The neuromorphic device 1250 may incorporate a storage interface 1299 bywhich neural network configuration data 1293 that is descriptive ofvarious parameters and hyperparameters of the neural network 1200 may bestored and/or retrieved. More specifically, the neural networkconfiguration data 1293 may include such parameters as weighting and/orbiasing values derived through the training of the neural network 1200,as has been described. Alternatively or additionally, the neural networkconfiguration data 1293 may include such hyperparameters as the mannerin which the neurons 1208 are to be interconnected (e.g., feed-forwardor recurrent), the trigger function to be implemented within the neurons1208, the quantity of layers and/or the overall quantity of the neurons1208. The neural network configuration data 1293 may provide suchinformation for more than one neuromorphic device 1250 where multipleones have been interconnected to support larger neural networks.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedilyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). Such processors may also provide an energy savingswhen compared to generic CPUs. For example, some of these processors caninclude a graphical processing unit (GPU), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), anartificial intelligence (AI) accelerator, a neural computing core, aneural computing engine, a neural processing unit, a purpose-built chiparchitecture for deep learning, and/or some other machine-learningspecific processor that implements a machine learning approach or one ormore neural networks using semiconductor (e.g., silicon (Si), galliumarsenide(GaAs)) devices. These processors may also be employed inheterogeneous computing architectures with a number of and/or a varietyof different types of cores, engines, nodes, and/or layers to achievevarious energy efficiencies, processing speed improvements, datacommunication speed improvements, and/or data efficiency targets andimprovements throughout various parts of the system when compared to ahomogeneous computing architecture that employs CPUs for general purposecomputing.

FIG. 13 illustrates various aspects of the use of containers 1336 as amechanism to allocate processing, storage and/or other resources of aprocessing system 1300 to the performance of various analyses. Morespecifically, in a processing system 1300 that includes one or more nodedevices 1330 (e.g., the aforementioned grid system 400), the processing,storage and/or other resources of each node device 1330 may be allocatedthrough the instantiation and/or maintenance of multiple containers 1336within the node devices 1330 to support the performance(s) of one ormore analyses. As each container 1336 is instantiated, predeterminedamounts of processing, storage and/or other resources may be allocatedthereto as part of creating an execution environment therein in whichone or more executable routines 1334 may be executed to cause theperformance of part or all of each analysis that is requested to beperformed.

It may be that at least a subset of the containers 1336 are eachallocated a similar combination and amounts of resources so that each isof a similar configuration with a similar range of capabilities, andtherefore, are interchangeable. This may be done in embodiments in whichit is desired to have at least such a subset of the containers 1336already instantiated prior to the receipt of requests to performanalyses, and thus, prior to the specific resource requirements of eachof those analyses being known.

Alternatively or additionally, it may be that at least a subset of thecontainers 1336 are not instantiated until after the processing system1300 receives requests to perform analyses where each request mayinclude indications of the resources required for one of those analyses.Such information concerning resource requirements may then be used toguide the selection of resources and/or the amount of each resourceallocated to each such container 1336. As a result, it may be that oneor more of the containers 1336 are caused to have somewhat specializedconfigurations such that there may be differing types of containers tosupport the performance of different analyses and/or different portionsof analyses.

It may be that the entirety of the logic of a requested analysis isimplemented within a single executable routine 1334. In suchembodiments, it may be that the entirety of that analysis is performedwithin a single container 1336 as that single executable routine 1334 isexecuted therein. However, it may be that such a single executableroutine 1334, when executed, is at least intended to cause theinstantiation of multiple instances of itself that are intended to beexecuted at least partially in parallel. This may result in theexecution of multiple instances of such an executable routine 1334within a single container 1336 and/or across multiple containers 1336.

Alternatively or additionally, it may be that the logic of a requestedanalysis is implemented with multiple differing executable routines1334. In such embodiments, it may be that at least a subset of suchdiffering executable routines 1334 are executed within a singlecontainer 1336. However, it may be that the execution of at least asubset of such differing executable routines 1334 is distributed acrossmultiple containers 1336.

Where an executable routine 1334 of an analysis is under development,and/or is under scrutiny to confirm its functionality, it may be thatthe container 1336 within which that executable routine 1334 is to beexecuted is additionally configured assist in limiting and/or monitoringaspects of the functionality of that executable routine 1334. Morespecifically, the execution environment provided by such a container1336 may be configured to enforce limitations on accesses that areallowed to be made to memory and/or 1/O addresses to control whatstorage locations and/or I/O devices may be accessible to thatexecutable routine 1334. Such limitations may be derived based oncomments within the programming code of the executable routine 1334and/or other information that describes what functionality theexecutable routine 1334 is expected to have, including what memoryand/or 1/O accesses are expected to be made when the executable routine1334 is executed. Then, when the executable routine 1334 is executedwithin such a container 1336, the accesses that are attempted to be madeby the executable routine 1334 may be monitored to identify any behaviorthat deviates from what is expected.

Where the possibility exists that different executable routines 1334 maybe written in different programming languages, it may be that differentsubsets of containers 1336 are configured to support differentprogramming languages. In such embodiments, it may be that eachexecutable routine 1334 is analyzed to identify what programminglanguage it is written in, and then what container 1336 is assigned tosupport the execution of that executable routine 1334 may be at leastpartially based on the identified programming language. Where thepossibility exists that a single requested analysis may be based on theexecution of multiple executable routines 1334 that may each be writtenin a different programming language, it may be that at least a subset ofthe containers 1336 are configured to support the performance of variousdata structure and/or data format conversion operations to enable a dataobject output by one executable routine 1334 written in one programminglanguage to be accepted as an input to another executable routine 1334written in another programming language.

As depicted, at least a subset of the containers 1336 may beinstantiated within one or more VMs 1331 that may be instantiated withinone or more node devices 1330. Thus, in some embodiments, it may be thatthe processing, storage and/or other resources of at least one nodedevice 1330 may be partially allocated through the instantiation of oneor more VMs 1331, and then in turn, may be further allocated within atleast one VM 1331 through the instantiation of one or more containers1336.

In some embodiments, it may be that such a nested allocation ofresources may be carried out to effect an allocation of resources basedon two differing criteria. By way of example, it may be that theinstantiation of VMs 1331 is used to allocate the resources of a nodedevice 1330 to multiple users or groups of users in accordance with anyof a variety of service agreements by which amounts of processing,storage and/or other resources are paid for each such user or group ofusers. Then, within each VM 1331 or set of VMs 1331 that is allocated toa particular user or group of users, containers 1336 may be allocated todistribute the resources allocated to each VM 1331 among variousanalyses that are requested to be performed by that particular user orgroup of users.

As depicted, where the processing system 1300 includes more than onenode device 1330, the processing system 1300 may also include at leastone control device 1350 within which one or more control routines 1354may be executed to control various aspects of the use of the nodedevice(s) 1330 to perform requested analyses. By way of example, it maybe that at least one control routine 1354 implements logic to controlthe allocation of the processing, storage and/or other resources of eachnode device 1330 to each VM 1331 and/or container 1336 that isinstantiated therein. Thus, it may be the control device(s) 1350 thateffects a nested allocation of resources, such as the aforementionedexample allocation of resources based on two differing criteria.

As also depicted, the processing system 1300 may also include one ormore distinct requesting devices 1370 from which requests to performanalyses may be received by the control device(s) 1350. Thus, and by wayof example, it may be that at least one control routine 1354 implementslogic to monitor for the receipt of requests from authorized usersand/or groups of users for various analyses to be performed using theprocessing, storage and/or other resources of the node device(s) 1330 ofthe processing system 1300. The control device(s) 1350 may receiveindications of the availability of resources, the status of theperformances of analyses that are already underway, and/or still otherstatus information from the node device(s) 1330 in response to polling,at a recurring interval of time, and/or in response to the occurrence ofvarious preselected events. More specifically, the control device(s)1350 may receive indications of status for each container 1336, each VM1331 and/or each node device 1330. At least one control routine 1354 mayimplement logic that may use such information to select container(s)1336, VM(s) 1331 and/or node device(s) 1330 that are to be used in theexecution of the executable routine(s) 1334 associated with eachrequested analysis.

As further depicted, in some embodiments, the one or more controlroutines 1354 may be executed within one or more containers 1356 and/orwithin one or more VMs 1351 that may be instantiated within the one ormore control devices 1350. It may be that multiple instances of one ormore varieties of control routine 1354 may be executed within separatecontainers 1356, within separate VMs 1351 and/or within separate controldevices 1350 to better enable parallelized control over parallelperformances of requested analyses, to provide improved redundancyagainst failures for such control functions, and/or to separatediffering ones of the control routines 1354 that perform differentfunctions. By way of example, it may be that multiple instances of afirst variety of control routine 1354 that communicate with therequesting device(s) 1370 are executed in a first set of containers 1356instantiated within a first VM 1351, while multiple instances of asecond variety of control routine 1354 that control the allocation ofresources of the node device(s) 1330 are executed in a second set ofcontainers 1356 instantiated within a second VM 1351. It may be that thecontrol of the allocation of resources for performing requested analysesmay include deriving an order of performance of portions of eachrequested analysis based on such factors as data dependenciesthereamong, as well as allocating the use of containers 1336 in a mannerthat effectuates such a derived order of performance.

Where multiple instances of control routine 1354 are used to control theallocation of resources for performing requested analyses, such as theassignment of individual ones of the containers 1336 to be used inexecuting executable routines 1334 of each of multiple requestedanalyses, it may be that each requested analysis is assigned to becontrolled by just one of the instances of control routine 1354. Thismay be done as part of treating each requested analysis as one or more“ACID transactions” that each have the four properties of atomicity,consistency, isolation and durability such that a single instance ofcontrol routine 1354 is given full control over the entirety of eachsuch transaction to better ensure that all of each such transaction iseither entirely performed or is entirely not performed. Allowing partialperformances to occur may cause cache incoherencies and/or datacorruption issues.

As additionally depicted, the control device(s) 1350 may communicatewith the requesting device(s) 1370 and with the node device(s) 1330through portions of a network 1399 extending thereamong. Again, such anetwork as the depicted network 1399 may be based on any of a variety ofwired and/or wireless technologies, and may employ any of a variety ofprotocols by which commands, status, data and/or still other varietiesof information may be exchanged. It may be that one or more instances ofa control routine 1354 cause the instantiation and maintenance of a webportal or other variety of portal that is based on any of a variety ofcommunication protocols, etc. (e.g., a restful API). Through such aportal, requests for the performance of various analyses may be receivedfrom requesting device(s) 1370, and/or the results of such requestedanalyses may be provided thereto. Alternatively or additionally, it maybe that one or more instances of a control routine 1354 cause theinstantiation of and maintenance of a message passing interface and/ormessage queues. Through such an interface and/or queues, individualcontainers 1336 may each be assigned to execute at least one executableroutine 1334 associated with a requested analysis to cause theperformance of at least a portion of that analysis.

Although not specifically depicted, it may be that at least one controlroutine 1354 may include logic to implement a form of management of thecontainers 1336 based on the Kubernetes container management platformpromulgated by Could Native Computing Foundation of San Francisco, CA,USA. In such embodiments, containers 1336 in which executable routines1334 of requested analyses may be instantiated within “pods” (notspecifically shown) in which other containers may also be instantiatedfor the execution of other supporting routines. Such supporting routinesmay cooperate with control routine(s) 1354 to implement a communicationsprotocol with the control device(s) 1350 via the network 1399 (e.g., amessage passing interface, one or more message queues, etc.).Alternatively or additionally, such supporting routines may serve toprovide access to one or more storage repositories (not specificallyshown) in which at least data objects may be stored for use inperforming the requested analyses.

FIG. 14 shows a block diagram of an example of a system 1400 accordingto some aspects of the present disclosure. The system 1400 is adistributed computing environment, such as a computing cluster, a datagrid, or a cloud computing environment. The system 1400 can include anynumber of computing nodes (e.g., physical machines), such as nodes 1406a-n. Examples of the nodes 1406 a-n can include servers, desktopcomputers, etc.

The system 1400 can include a reconciliation orchestrator 1404. Thereconciliation orchestrator 1404 can be any software that is configuredto orchestrate a reconciliation process for a set of forecasts 1402across some or all of the nodes 1406 a-n. The reconciliationorchestrator 1404 may also be configured to perform other tasks, such asjob scheduling or workload balancing. In some examples, the set offorecasts 1402 may be time series that all span the same time period andmay have the same time interval between data points. A time series canbe a sequence of data points indexed in time order, where the datapoints are collected at uniform time intervals. The forecasts 1402 canhave a hierarchical relationship to one another, such that someforecasts are children of other forecasts. The reconciliation processcan be configured to adjust the set of forecasts 1402 to minimize aweighted sum of variances of reconciled forecast errors associated withall forecasts (e.g., time series) in the hierarchy.

More specifically, the reconciliation orchestrator 1404 can receive theset of forecasts 1402. The forecasts 1402 may have been generated by amachine-learning model, such as an autoregressive integrated movingaverage (ARIMA) model or an exponential smoothing model (ESM). The setof forecasts 1402 may include any number of forecasts, such as three ormore forecasts. The forecasts 1402 may each be time-stamped data (e.g.,a time series) of predicted values over a future time window.

The forecasts 1402 can have a hierarchical relationship to one another.One example of such a hierarchy of forecasts is shown in FIGS. 15-16 .FIG. 15 shows a simplified representation of the forecasts as blocks.FIG. 16 shows the forecasts as time series graphs. In FIGS. 15-16 ,there are three levels of forecasts in the hierarchies 1500, 1600. Thebasal level (e.g., Level 2) has five forecasts—AA, AB, AC, BA, and BB.The middle level (e.g., Level 1) has two forecasts—A and B. The toplevel (e.g., Level 0) has one forecast—Total.

Still referring to FIGS. 15-16 , it may be desirable for the forecaststo satisfy an aggregation constraint, for example so that the depictedconditions 1502 are satisfied at every time t. In particular, at a giventime t, the value in the Total forecast should be the sum of the valuesin the intermediate forecasts A and B. Similarly, at that time t, thevalue in forecast A should be the sum of the values in the basalforecasts AA, AB, and AC. And, at that time t, the value in forecast Bshould be the sum of the values in the basal forecasts BA and BB. As onespecific example, the forecasts may relate to a company's sales. Thesales in region A should be the sum of the sales in locations (e.g.,stores) AA, AB, and AC. The sales in region B should be the sum of thesales in locations BA and BB. And the total sales should be the sum ofthe sales in regions A and B. Of course, other examples may involveother hierarchical arrangements of more or fewer forecasts.

In some examples, the reconciliation orchestrator 1404 can receive theset of forecasts 1402 in a data table format. For example, the forecasts1402 can be stored in data tables that are arranged by level of thehierarchy. There can be one data table per level of the hierarchy, wherethe data table includes all of the data points associated with all ofthe forecasts at that level of the hierarchy. For instance, referring toFIG. 17 , there can be a first data table 1702 that includes all of thedata points in all of the forecasts at a first level of the hierarchy(e.g., AA, AB, AC, BA, and BB). There can be a second data table 1704that includes all of the data points in all of the forecasts at a secondlevel of the hierarchy (e.g., A and B). And there can be a third datatable 1706 that includes all of the data points in all of the forecastsat a third level of the hierarchy (e.g., Total). Each row of each datatable can correspond to a particular data point in a particular forecastcovered by that data table. For example, row 1708 can correspond to aparticular data point in forecast AA. Organizing the forecasts 1402 intodata tables by level in the hierarchy can make it easier for thereconciliation orchestrator 1404 to divide the forecasts 1402 intodatasets by timestamp, as described below.

Referring back to FIG. 14 , after receiving the set of forecasts 1402,the reconciliation orchestrator 1404 can determine how to divide anddistribute the set of forecasts 1402 among the nodes 1406 a-n toimplement the reconciliation process. In some examples, thereconciliation orchestrator 1404 can divide the set of forecasts 1402into datasets 1410 by timestamp, where each dataset corresponds to asingle time point in the set of forecasts 1402. For example, the set offorecasts 1402 have a ten minute interval between data points. Thereconciliation orchestrator 1404 can generate a first dataset 1410 a-1that consists of the data points from the set of forecasts 1402corresponding to Jun. 22, 2024 at 1:00 PM. The reconciliationorchestrator 1404 can also generate a second dataset 1410 a-2 thatconsists of the data points from the set of forecasts 1402 correspondingto Jun. 22, 2024 at 1:10 PM. And the reconciliation orchestrator 1404can generate a third dataset 1410 b-1 that consists of the data pointsfrom the set of forecasts 1402 corresponding to Jun. 22, 2024 at 1:20PM. And so on for all of the data points in the set of forecasts 1402.One specific example of this process is shown in FIG. 18 . In FIG. 18 ,a data point at time t1 is identified by a dotted line in each of thedepicted forecasts. Those data points are collected into a dataset 1802corresponding to time t1. That dataset 1802 can be referred to hereinusing the mathematical notation y_(t), where t is a point in time. Abasal dataset 1804 may also be determined. The basal dataset 1804 mayonly consist of the data points from the basal level forecasts (and notthe higher-level forecasts). The basal dataset 1804 can be referred toherein using the mathematical notation y_(basal,t).

After dividing the set of forecasts 1402 into the datasets 1410, thereconciliation orchestrator 1404 can distribute the datasets 1410 amongthe nodes 1406 a-n. The reconciliation orchestrator 1404 can distributethe datasets 1410 among the nodes 1406 a-n in any suitable manner. Forexample, the reconciliation orchestrator 1404 can distribute thedatasets 1410 substantially evenly among the nodes 1406 a-n, so thatmost or all of the nodes 1406 a-n have the same number of datasets. Asanother example, the reconciliation orchestrator 1404 can distribute thedatasets 1410 based on the existing workloads of the nodes 1406 a-n, forexample so that nodes with less available computing resources mayreceive fewer of the datasets 1410 than nodes with more availablecomputing resources. This can help prevent against overburdening a nodethat is already processing a large number of workloads.

The nodes 1406 a-n can each receive one or more of the datasets 1410from the reconciliation orchestrator 1404. After receiving the datasets1410, the nodes 1406 a-n can assign the datasets 1410 to separateprocessing threads 1408. For example, node 1406 a can receive twodatasets 1410 a-1, 1410 a-2 from the reconciliation orchestrator 1404and assign them to threads 1408 a-1, 1408 a-2. As another example, node1406 b can receive one dataset 1410 b-1 from the reconciliationorchestrator 1404 and assign it to thread 1408 b-1. As yet anotherexample, node 1406 n can receive three datasets 1410 n-1, 1410 n-2, 1410n-3 from the reconciliation orchestrator 1404 and assign them to threads1408 n-1, 1408 n-2, 1408 n-3. The nodes 1406 a-n may assign the datasets1410 to threads 1408 that are not already processing any workloads, sothat the threads 1408 are dedicated for the reconciliation process. Insome examples, the nodes 1406 a-n may generate new threads 1408 that aresolely dedicated to the reconciliation process and shut down thosethreads when the reconciliation process is complete. This may conservecomputing resources.

The threads 1408 can each execute a respective reconciliation process ona respective dataset 1410 to produce one or more reconciled values 1412(e.g., reconciled data points). The reconciled values 1412 are denotedas “RV” in FIG. 14 . For example, thread 1408 a-1 can execute areconciliation process on dataset 1410 a-1 to produce a reconciled value1412 a-1. Thread 1408 a-2 can execute a reconciliation process ondataset 1410 a-2 to produce a reconciled value 1412 a-2. Thread 1408 b-1can execute a reconciliation process on dataset 1410 b-1 to produce areconciled value 1412 b-1. And so on through thread 1408 n-3. Some orall of the threads 1408 across some or all of the nodes 1406 can executetheir reconciliation processes in parallel to one another, so that thereconciliation processes are performed concurrently (e.g.,simultaneously). The nodes 1406 a-n can then transmit the reconciledvalues 1412 to the reconciliation orchestrator 1404.

The reconciliation orchestrator 1404 can collect the reconciled values1412 and generate a set of reconciled forecasts 1418 based on thereconciled values 1412. The set of reconciled forecasts 1418 can bereconciled versions of the original set of forecasts 1402. Thereconciliation orchestrator 1404 can then provide the set of reconciledforecasts 1418 for subsequent use. For example, the reconciliationorchestrator 1404 can transmit the set of reconciled forecasts 1418 to aclient device for use by an application executing on the client device.In some examples, the client device may have provided the original setof forecasts 1402 to the system 1400 for reconciliation and can receivethe set of reconciled forecasts 1418 in return from the system 1400.

Through the above process, the reconciliation orchestrator 1404 candivide and distribute the reconciliation process among the nodes 1406a-n, which in turn can further distribute the reconciliation processamong separate threads 1408. This can result in two levels ofparallelism that can improve the efficiency of the reconciliationprocess and significantly expedite the reconciliation process.

As noted above, the nodes 1406 a-n can each perform reconciliationprocesses on their assigned datasets 1410. To perform the reconciliationprocesses, the nodes 1406 a-n may each compute a respective summationmatrix (“S-matrix”) 1414 that can encode the aggregation (e.g.,summation) constraints. To compute an S-matrix, a node 1406 a can accessone of its assigned datasets and generate the S-matrix based on thatdataset. For example, node 1406 a can generate an S-matrix 1414 a basedon dataset 1410 a-1, node 1406 b can generate an S-matrix 1414 b basedon dataset 1410 b-1, and node 1406 n can generate the S-matrix 1414 nbased on dataset 1410 n-1.

In some examples, the S-matrix 1414 can have as many rows as there areforecasts in the set of forecasts 1402. The S-matrix 1414 may also haveas many columns as there are basal level forecasts in the set offorecasts 1402. One specific example is shown in FIG. 19 . As shown, ifthere are eight total forecasts in the set of forecasts 1402, of whichfive are basal level forecasts, the S-matrix 1414 can be 8×5 in size. Ofcourse, depending on the implementation, the number of the rows andcolumns can be swapped (e.g., so that the S-matrix is 5x8) in otherexamples. The S-matrix 1414 can include binary values that encode one ormore aggregation constraints.

After generating the S-matrices 1414, some or all of the nodes 1406 a-ncan compare their computed S-matrices 1414 to one another to ensure thatthey match. For example, node 1406 a can request the S-matrix 1414 bfrom node 1406 b. Node 1406 a can then compare its S-matrix 1414 a tothe other S-matrix 1414 b. If they do not match, it may signify anonuniformity in the datasets 1410 that could lead to downstream errorsand/or inaccuracies in the reconciliation process. So, the node 1406 acan transmit an error message 1420 to the reconciliation orchestrator1404. In some examples, the reconciliation orchestrator 1404 can receivethe error message 1420 and responsively transmit an error notificationto one or more client devices of one or more users. The users caninclude an administrator and/or the user that submitted the set offorecasts 1402 for reconciliation. The error notification can notify theone or more users of the problem. Additionally or alternatively, inresponse to receiving the error message 1420, the reconciliationorchestrator 1404 may automatically perform one or more operations in aneffort to resolve the problem. For example, the reconciliationorchestrator 1404 may identify the root cause of the problem, such as amissing value in the dataset 1410 b-1, and update the dataset 1410 b-1to include the missing value. This may involve generating a syntheticvalue to take the place of the missing value. The reconciliationorchestrator 1404 may then transmit the updated dataset to the node 1406b, which can recompute the S-matrix 1414 b and perform the validationprocess again. This initial validation of the S-matrices 1414 a-n canhelp avoid downstream inaccuracies that could occur if differentreconciliation processes are performed on different nodes usingdifferent S-matrices.

After generating the S-matrices 1414 a-n, the nodes 1406 a-n can eachgenerate a respective reconciliation matrix, which is referred to hereinas a “G-matrix”. In some examples, the nodes 1406 a-n can generate theG-matrices 1416 based on the S-matrices 1414 and a weighting matrix. Theweighting matrix can be a predefined matrix of weights. For instance,the nodes 1406 a-n can each generate a G-matrix 1416 according to thefollowing equation:G=(S′WS)⁻¹ S′Wwhere G is the G-matrix, S is the S-matrix, and W is a predefinedweighting matrix. Thus, the G-matrix on each of the nodes 1406 a-n candepend on the S-matrix 1414 computed by that node. For example, node1406 a can generate a G-matrix 1416 a based on its S-matrix 1414 a andoptionally the weighting matrix. Node 1406 b can generate a G-matrix1416 b based on its S-matrix 1414 b and optionally the weighting matrix.And node 1406 n can generate a G-matrix 1416 n based on its S-matrix1414 n and optionally the weighting matrix.

After generating the G-matrices 1416, the nodes 1406 a-n can use theG-matrices 1416 to perform their respective reconciliation processes.For example, the nodes 1406 a-n can compute the reconciled basal levelforecasts according to the following equation:Z _(basal,t) =Gŷ _(t)where Z_(basal,t) includes the reconciled basal level forecast values, Gis the G-matrix, and y_(t) includes the original forecast values at alllevels of the hierarchy. The nodes 1406 a-n can then compute the fullreconciled forecasts according to the following equation:Z _(t) =SZ _(basal,t) =SGŷ _(t)where Z_(t) includes the full reconciled forecast values, Z_(basal,t)includes the reconciled basal level forecast values, and S is theS-matrix. After generating the full reconciled forecast values (e.g.,reconciled values 1412), the nodes 1406 a-n can transmit them to thereconciliation orchestrator 1404 as described above.

In some examples, a thread 1408 may be assigned to process a dataset1410 that is missing a value for a data point. For example, the dataset1410 b-1 may include a data point from a forecast, where the data pointcorresponds to Jun. 22, 2024 at 1:00 PM. But, the data point may bemissing a value (e.g., the value may be NULL). This can happen forvarious reasons, for example if the forecasting model that generated theforecast experienced an error or did not have sufficient information tocreate a value for the data point. To prevent the missing value fromcausing a downstream error, in some examples the corresponding thread1408 b-1 can dynamically adjust the corresponding S-matrix 1414 b toaccount for the missing value. If there is a weighting matrix (W), theweighting matrix can also be dynamically adjusted to account for themissing value. One example of adjusting an S-matrix 2004 is shown inFIG. 20 . As shown in FIG. 20 , the thread 1408 b-1 can dynamicallycreate a modified S-matrix 2004 that accounts for the missing value. Thethread 1408 b-1 can also dynamically create a modified G-matrix 2006based on the modified S-matrix 2004, for example by recomputing theG-matrix using the equation described above. In some examples involvinga weighting matrix (W), the W-matrix can also be dynamically adjusted toaccount for the missing value. The thread 1408 b-1 can then dynamicallycreate the modified G-matrix 2006 based on the modified S-matrix and themodified W-matrix. The modified S-matrix 2004, the modified W-matrix,and the modified G-matrix may be new matrices created separately fromthe originals, so that other threads on the node 1406 b can still usethe original S-matrix 1414 b, W-matrix, and G-matrix 1416 b in theirreconciliation processes. The thread 1408 b-1 can then use the modifiedS-matrix 2004, the modified W-matrix, and the modified G-matrix 2006 inits reconciliation process.

FIG. 21 shows an example of a process for creating a modified S-matrix2004. In this example, the dataset 2102 can include data points from allof the forecasts shown in FIG. 15 for time t. If all of the data pointshad valid values, it could result in the S-matrix 2104. But in thisexample, the data point value for forecast AC is missing (e.g., NULL).So, the data point with the missing value can be excluded from thedataset 2102 to produce a modified dataset 2106. A modified S-matrix2108 can then be generated based on the modified dataset 2106. Themodified S-matrix 2108 can exclude a row and column corresponding to themissing value. For illustrative purposes, the excluded row and column isshown in bold in S-matrix 2104. In this way, a modified S-matrix 2108can be generated to account for the missing value. A similar process canbe applied to the W-matrix (e.g., excluding a row and column associatedwith the missing value). The modified S-matrix and/or W-matrix can thenbe used to compute a modified G-matrix, as described above, for use inthe reconciliation process.

FIG. 22 shows a flowchart of an example of a process for dividing anddistributing forecast data among a group of nodes according to someaspects of the present disclosure. Other examples may include moreoperations, fewer operations, different operations, or a differentsequence of operations than is shown. The operations of FIG. 22 aredescribed below with reference to the components of FIG. 14 describedabove.

In block 2202, a system 1400 (e.g., reconciliation orchestrator 1404)can receive a set of forecasts 1402 that have a hierarchicalrelationship to one another. The system 1400 may receive the set offorecasts 1402 from a client device, which can be internal or externalto the system 1400. Examples of the client device can include a laptopcomputer, a desktop computer, a server, a wearable device (e.g., a smartwatch), a mobile phone, a tablet, or an e-reader. Each forecast amongthe set of forecasts 1402 corresponds to a respective level of thehierarchy. At least one forecast in the set of forecasts 1402 cancorrespond to a higher level of the hierarchy than at least one otherforecast of the set of forecasts 1402.

In block 2204, the system 1400 divides the set of forecasts 1402 by timepoint into a plurality of datasets 1410. Each dataset 1410 cancorrespond to a single time point. For example, each dataset 1410 mayonly include the data points corresponding to that single time pointfrom the set of forecasts 1402. In other examples, the system 1400 maydivide the forecasts 1402 into the datasets 1410 based on other factors,additionally or alternatively to time point.

In block 2206, the system 1400 distributes the plurality of datasets1410 among a plurality of computing nodes 1406 of a distributedcomputing system 1400. Because the data points in the forecasts 1402were grouped into the datasets 1410 by time point in block 2204, all ofthe data points corresponding to the same time point may be assigned tothe same computing node. The plurality of computing nodes 1406 can beconfigured to collectively process the set of forecasts 1402 in parallelto implement an overall reconciliation process that involves adjustingthe set of forecasts 1402 subject to one or more constraints, such as anaggregation constraint. Distributing the datasets 1410 among more nodesmay result in a faster execution of the reconciliation process thandistributing the datasets 1410 among fewer nodes.

FIG. 23 shows a flowchart of an example of a process for performingreconciliation on a computing node according to some aspects of thepresent disclosure. Other examples may include more operations, feweroperations, different operations, or a different sequence of operationsthan is shown. The operations of FIG. 23 are described below withreference to the components of FIG. 14 described above.

In block 2302, a computing node 1406 a receives a dataset 1410 a-1. Thedataset 1410 a-1 can include data points corresponding to a single timepoint in a set of forecasts 1402. The computing node 1406 a may receivethe dataset 1410 a-1 from a reconciliation orchestrator 1404 or anothersource.

In block 2304, the computing node 1406 a assigns the dataset 1410 a-1 toa processing thread 1408 a-1. In some examples, the processing thread1408 a-1 may be a new processing thread generated by the computing node1406 a to handle the dataset 1410 a-1.

In block 2306, the computing node 1406 a constructs an S-matrix 1414 abased on the dataset 1410 a. For example, the computing node 1406 a cangenerate an S-matrix 1414 a that has a number of rows and columns thatcorresponds to the number of forecasts represented in the dataset 1410a.

In block 2308, the computing node 1406 a validates the S-matrix 1414 a.This step may be performed using the process described below withrespect to FIG. 24 .

In block 2310, the computing node 1406 a constructs a G-matrix based onthe S-matrix and a W-matrix. This step may be performed using any of thetechniques described above.

In block 2312, the computing node 1406 a executes the processing thread.When executed, the processing thread can implement the steps describedbelow with respect to FIG. 25 .

As noted earlier, in some examples the computing node 1406 a can performan S-matrix validation process prior to executing the reconciliationprocesses on the threads 1408 a. FIG. 24 shows a flowchart of an exampleof such an S-matrix validation process according to some aspects of thepresent disclosure.

In block 2402, the computing node 1406 a receives one or more datasets1410 a associated with a set of forecasts 1402. The computing node 1406a can receive the one or more datasets 1410 a from a reconciliationorchestrator 1404, in some examples.

In block 2404, the computing node 1406 a selects one of the datasets1410 a and generates an S-matrix 1414 a based on the selected dataset1410 a-1.

In block 2406, the computing node 1406 a receives one or more otherS-matrices 1414 b-n from one or more other computing nodes 1406 b-n. Theone or more other S-matrices 1414 b-n may have been computed by the oneor more other computing nodes 1406 b-n based on the datasets 1410 b-nassigned to those computing nodes 1406 b-n.

In block 2408, the computing node 1406 a determines whether its S-matrix1414 a matches the one or more other S-matrices 1414 b-n. Two S-matricescan “match” if they are identical. If the S-matrix 1414 a matches theone or more other S-matrices 1414 b-n, then the process can proceed toblock 2410 where computing node 1406 a can proceed with thereconciliation process. Otherwise, the process can proceed to block 2412where the computing node 1406 a can output an error message 1420. Forexample, the computing node 1406 a can transmit the error message 1420to the reconciliation orchestrator 1404.

FIG. 25 shows a flowchart of an example of a process for performingreconciliation on processing thread of a computing node according tosome aspects of the present disclosure. Other examples may include moreoperations, fewer operations, different operations, or a differentsequence of operations than is shown. The operations of FIG. 25 aredescribed below with reference to the components of FIG. 14 describedabove.

In block 2502, a thread 1408 a-1 receives a dataset 1410 a-1. Thedataset 1410 a-1 can include data points corresponding to a single timepoint in a set of forecasts 1402.

In block 2504, the thread 1408 a-1 organizes the set of data points inthe dataset 1410 a-1 by forecast. For example, the thread 1408 a-1 cansort the data points by forecast. This may help ensure that some or allof the datasets 1410 in the system 1400 are organized the same way,which can prevent downstream problems and inaccuracies in thereconciliation process.

In block 2506, the thread 1408 a-1 compares the dataset 1410 a-1 to theS-matrix 1414 a on the computing node 1406 a to perform a furthervalidation. For example, the thread 1408 a-1 can compare the dataset1410 a-1 to the S-matrix 1414 a previously computed by the computed node1406 a, for example to make sure that all of forecasts (e.g., timeseries) used construct the S-matrix 1414 a exist in the dataset 1410 a,and vice versa. If not, the thread 1408 a-1 can throw an error.

In block 2508, the thread 1408 a-1 determines if there are any missingvalues in the dataset 1410 a-1. For example, the thread 1408 a-1 canidentify any data points that have an empty value or a value of “NULL”.If the thread 1408 a-1 determines that the dataset 1410 a-1 has a datapoint with a missing value, the process can proceed to block 2510.Otherwise, the process can proceed to block 2514.

In block 2510, the thread 1408 a-1 generates a modified S-matrix and amodified W-matrix that account for missing value. For example, thethread 1408 a-1 can generate a new S-matrix that excludes a row and/orcolumn of values associated with the forecast having the missing value.The thread 1408 a-1 can also generate a new W-matrix that excludes a rowand/or column of values associated with the forecast having the missingvalue.

In block 2512, the thread 1408 a-1 generates a modified G-matrix basedon the modified S-matrix and the modified W-matrix. For example, thethread 1408 a-1 can generate the modified G-matrix according to thefollowing equation:G _(modified)=(S _(modified) ′*W _(modified) *S _(modified))⁻¹ S′_(modified) *W _(modified)Where G_(modified) is the modified G-matrix, S_(modified) is themodified S-matrix, and W_(modified) is the modified weighting matrix.

In block 2514, the thread 1408 a-1 obtains an S-matrix 1414 a, aW-matrix, and a G-matrix 1416 a, some or all of which may havepreviously been computed. For example, the thread 1408 a-1 can obtain anS-matrix 1414 a and a G-matrix 1416 a that the computing node 1406 a mayhave previously computed based on the same dataset 1410 a-1 or adifferent dataset.

In block 2516, the thread 1408 a-1 executes a reconciliation process onthe dataset 1410 a-1 using the S-matrix, the W-matrix, and the G-matrix.The S-matrix can be the original or modified S-matrix, the W-matrix canbe the original or modified W-matrix, and the G-matrix can be theoriginal or modified G-matrix, depending on the result of block 2508.The reconciliation process can yield one or more reconciled values 1412a-1.

In block 2518, the thread 1408 a-1 outputs the one or more reconciledvalues 1412 a-1. For example, the thread 1408 a-1 can transmit the oneor more reconciled values 1412 a-1 via one or more networks, such as alocal area network or the Internet, to the reconciliation orchestrator1404 or a client device.

In the previous description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The FIGURES and description are notintended to be restrictive.

The previous description provides examples that are not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the previous description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the previous description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples may have been described as a process that isdepicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. And a process can have more or feweroperations than are depicted in a figure. A process can correspond to amethod, a function, a procedure, a subroutine, a subprogram, etc. When aprocess corresponds to a function, its termination can correspond to areturn of the function to the calling function or the main function.

Systems depicted in some of the FIGURES can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

The invention claimed is:
 1. A system comprising: one or moreprocessors; and one or more memories including program code that isexecutable by the one or more processors for causing the one or moreprocessors to: receive a plurality of forecasts that have a hierarchicalrelationship with respect to one another, wherein each forecast amongthe plurality of forecasts corresponds to a respective level of ahierarchy, and wherein at least one forecast in the plurality offorecasts corresponds to a higher level of the hierarchy than at leastone other forecast of the plurality of forecasts; and distribute theplurality of forecasts among a plurality of computing nodes of adistributed computing environment by time point, such that all datapoints corresponding to a same time point in the plurality of forecastsare assigned to a same computing node of the plurality of computingnodes, the plurality of computing nodes being configured to collectivelyprocess the plurality of forecasts in parallel to implement areconciliation process that involves adjusting the plurality offorecasts subject to an aggregation constraint, the plurality ofcomputing nodes being further configured to: receive a plurality ofdatasets corresponding to a plurality of time points, each dataset ofthe plurality of datasets including a respective set of data points fromthe plurality of forecasts corresponding to a single time point;organize the respective set of data points in each of the plurality ofdatasets by forecast to generate a plurality of ordered datasets; assignthe plurality of ordered datasets to a plurality of processing threadson the plurality of computing nodes, the plurality of processing threadsbeing executable in parallel to implement respective portions of thereconciliation process using the plurality of ordered datasets; executethe plurality of processing threads to implement the reconciliationprocess on the plurality of forecasts, to thereby generate a pluralityof reconciled values; and output the plurality of reconciled values. 2.The system of claim 1, wherein the plurality of forecasts span a sametime period and have a same time interval.
 3. The system of claim 1,wherein a computing node in the plurality of computing nodes isconfigured to: access a dataset of the plurality of datasets, whereinthe dataset is assigned to the computing node and corresponds to aparticular time point; generate, based on the dataset, a summationmatrix that encodes aggregation constraints; determine at least oneother summation matrix generated by at least one other computing node ofthe plurality of computing nodes; and perform a comparison of thesummation matrix against the at least one other summation matrix.
 4. Thesystem of claim 3, wherein the computing node is further configured to:based on the comparison, determine that the summation matrix matches theat least one other summation matrix; and in response to determining thatthe summation matrix matches the at least one other summation matrix,generate reconciled values associated with the particular time pointbased on the summation matrix and the dataset.
 5. The system of claim 3,wherein the computing node is further configured to: based on thecomparison, determine that the summation matrix does not match the atleast one other summation matrix; and based on determining that thesummation matrix does not match the at least one other summation matrix,output an error message.
 6. The system of claim 3, wherein the computingnode is further configured to: generate a reconciliation matrix based onthe summation matrix and a weighting matrix, wherein the weightingmatrix is distinct from the summation matrix; and generate reconciledvalues associated with the particular time point based on thereconciliation matrix, the summation matrix, and the dataset.
 7. Thesystem of claim 3, wherein a processing thread of the computing node isfurther configured to: determine that the dataset includes a data pointwith a missing value, the data point being part of a forecast of theplurality of forecasts; and based on determine that the dataset has thedata point with the missing value: adjust the summation matrix byremoving a column and a row associated with the data point from thesummation matrix, to thereby create an adjusted summation matrix; adjusta weighting matrix by removing a column and a row associated with thedata point from the weighting matrix, to thereby create an adjustedweighting matrix; generate a reconciliation matrix based on the adjustedsummation matrix and the adjusted weighting matrix; and generatereconciled values associated with the particular time point based on thereconciliation matrix and the dataset.
 8. The system of claim 3, whereinthe one or more memories further include program code that is executableby the one or more processors for causing the one or more processors to:receive a plurality of data tables, each data table of the plurality ofdata tables corresponding to a respective level of the hierarchy andincluding all data points from all forecasts of the plurality offorecasts that correspond to that respective level of the hierarchy,wherein each row of each data table of the plurality of data tablescorresponds to a single data point of a single forecast of the pluralityof forecasts; and generate a dataset, of the plurality of datasets, byextracting a set of datapoints corresponding to the same time point fromthe plurality of data tables and grouping them together as the dataset.9. The system of claim 1, wherein the reconciliation process isconfigured to adjust the plurality of forecasts to minimize a weightedsum of variances of reconciled forecast errors associated with allforecasts in the hierarchy.
 10. The system of claim 1, wherein thereconciliation process is configured to determine the plurality ofreconciled values by: multiplying a summation matrix by a reconciliationmatrix to produce a product; and multiplying the product by theplurality of forecasts.
 11. The system of claim 1, wherein the pluralityof computing nodes are configured to employ two levels of parallelism toexpedite the reconciliation process, the two levels of parallelismincluding a first level of parallelism in which the plurality offorecasts are divided by timestamp into subsets for processing inparallel across the plurality of computing nodes, and a second level ofparallelism in which multiple subsets assigned to each individualcomputing node of the plurality of computing nodes are processed inparallel by multiple processing threads on that individual computingnode.
 12. A method comprising: receiving, by one or more processors, aplurality of forecasts that have a hierarchical relationship withrespect to one another, wherein each forecast among the plurality offorecasts corresponds to a respective level of a hierarchy, and whereinat least one forecast in the plurality of forecasts corresponds to ahigher level of the hierarchy than at least one other forecast of theplurality of forecasts; and distributing, by the one or more processors,the plurality of forecasts among a plurality of computing nodes of adistributed computing environment by time point, such that all datapoints corresponding to a same time point in the plurality of forecastsare assigned to a same computing node of the plurality of computingnodes, the plurality of computing nodes being configured to collectivelyprocess the plurality of forecasts in parallel to implement areconciliation process that involves adjusting the plurality offorecasts subject to an aggregation constraint, the plurality ofcomputing nodes being further configured to: receive a plurality ofdatasets corresponding to a plurality of time points, each dataset ofthe plurality of datasets including a respective set of data points fromthe plurality of forecasts corresponding to a single time point;organize the respective set of data points in each of the plurality ofdatasets by forecast to generate a plurality of ordered datasets; assignthe plurality of ordered datasets to a plurality of processing threadson the plurality of computing nodes, the plurality of processing threadsbeing executable in parallel to implement respective portions of thereconciliation process using the plurality of ordered datasets; executethe plurality of processing threads to implement the reconciliationprocess on the plurality of forecasts, to thereby generate a pluralityof reconciled values; and output the plurality of reconciled values. 13.The method of claim 12, wherein the plurality of forecasts span a sametime period.
 14. The method of claim 12, wherein a computing node in theplurality of computing nodes is configured to: access a dataset of theplurality of datasets, wherein the dataset is assigned to the computingnode and corresponds to a particular time point; generate, based on thedataset, a summation matrix that encodes aggregation constraints;determine at least one other summation matrix generated by at least oneother computing node of the plurality of computing nodes; and perform acomparison of the summation matrix against the at least one othersummation matrix.
 15. The method of claim 14, wherein the computing nodeis further configured to: based on the comparison, determine that thesummation matrix matches the at least one other summation matrix; and inresponse to determining that the summation matrix matches the at leastone other summation matrix, generate reconciled values associated withthe particular time point based on the summation matrix and the dataset.16. The method of claim 14, wherein the computing node is furtherconfigured to: based on the comparison, determine that the summationmatrix does not match the at least one other summation matrix; and basedon determining that the summation matrix does not match the at least oneother summation matrix, output an error message.
 17. The method of claim14, wherein the computing node is further configured to: generate areconciliation matrix based on the summation matrix and a weightingmatrix, wherein the weighting matrix is distinct from the summationmatrix; and generate reconciled values associated with the particulartime point based on the reconciliation matrix, the summation matrix, andthe dataset.
 18. The method of claim 14, wherein a processing thread ofthe computing node is further configured to: determine that the datasetincludes a data point with a missing value, the data point being part ofa forecast of the plurality of forecasts; and based on determine thatthe dataset has the data point with the missing value: adjust thesummation matrix by removing a column and a row associated with the datapoint from the summation matrix, to thereby create an adjusted summationmatrix; generate a reconciliation matrix based on the adjusted summationmatrix and a weighting matrix; and generate reconciled values associatedwith the particular time point based on the reconciliation matrix andthe dataset.
 19. The method of claim 14, further comprising: receiving aplurality of data tables, each data table of the plurality of datatables corresponding to a respective level of the hierarchy andincluding all data points from all forecasts of the plurality offorecasts that correspond to that respective level of the hierarchy,wherein each row of each data table of the plurality of data tablescorresponds to a single data point of a single forecast of the pluralityof forecasts; and generating a dataset, of the plurality of datasets, byextracting a set of datapoints corresponding to the same time point fromthe plurality of data tables and grouping them together as the dataset.20. The method of claim 14, wherein the reconciliation process isconfigured to adjust the plurality of forecasts to minimize a weightedsum of variances of reconciled forecast errors associated with allforecasts in the hierarchy.
 21. The method of claim 14, wherein thereconciliation process is configured to determine the plurality ofreconciled values by: multiplying a summation matrix by a reconciliationmatrix to produce a product; and multiplying the product by theplurality of forecasts.
 22. The method of claim 14, wherein theplurality of computing nodes are configured to employ two levels ofparallelism to expedite the reconciliation process, the two levels ofparallelism including a first level of parallelism in which theplurality of forecasts are divided by timestamp into subsets forprocessing in parallel across the plurality of computing nodes, and asecond level of parallelism in which multiple subsets assigned to eachindividual computing node of the plurality of computing nodes areprocessed in parallel by multiple processing threads on that individualcomputing node.
 23. A non-transitory computer-readable medium comprisingprogram code that is executable by one or more processors for causingthe one or more processors to: receive a plurality of forecasts thathave a hierarchical relationship with respect to one another, whereineach forecast among the plurality of forecasts corresponds to arespective level of a hierarchy, and wherein at least one forecast inthe plurality of forecasts corresponds to a higher level of thehierarchy than at least one other forecast of the plurality offorecasts; and distribute the plurality of forecasts among a pluralityof computing nodes of a distributed computing environment by time point,such that all data points corresponding to a same time point in theplurality of forecasts are assigned to a same computing node of theplurality of computing nodes, the plurality of computing nodes beingconfigured to collectively process the plurality of forecasts inparallel to implement a reconciliation process that involves adjustingthe plurality of forecasts subject to an aggregation constraint, theplurality of computing nodes being further configured to: receive aplurality of datasets corresponding to a plurality of time points, eachdataset of the plurality of datasets including a respective set of datapoints from the plurality of forecasts corresponding to a single timepoint; organize the respective set of data points in each of theplurality of datasets by forecast to generate a plurality of ordereddatasets; assign the plurality of ordered datasets to a plurality ofprocessing threads on the plurality of computing nodes, the plurality ofprocessing threads being executable in parallel to implement respectiveportions of the reconciliation process using the plurality of ordereddatasets; execute the plurality of processing threads to implement thereconciliation process on the plurality of forecasts, to therebygenerate a plurality of reconciled values; and output the plurality ofreconciled values.
 24. The non-transitory computer-readable medium ofclaim 23, wherein a computing node in the plurality of computing nodesis configured to: access a dataset of the plurality of datasets, whereinthe dataset is assigned to the computing node and corresponds to aparticular time point; generate, based on the dataset, a summationmatrix that encodes aggregation constraints; determine at least oneother summation matrix generated by at least one other computing node ofthe plurality of computing nodes; and perform a comparison of thesummation matrix against the at least one other summation matrix. 25.The non-transitory computer-readable medium of claim 24, wherein thecomputing node is further configured to: based on the comparison,determine that the summation matrix matches the at least one othersummation matrix; and in response to determining that the summationmatrix matches the at least one other summation matrix, generatereconciled values associated with the particular time point based on thesummation matrix and the dataset.
 26. The non-transitorycomputer-readable medium of claim 24, wherein the computing node isfurther configured to: based on the comparison, determine that thesummation matrix does not match the at least one other summation matrix;and based on determining that the summation matrix does not match the atleast one other summation matrix, output an error message.
 27. Thenon-transitory computer-readable medium of claim 24, wherein thecomputing node is further configured to: generate a reconciliationmatrix based on the summation matrix and a weighting matrix, wherein theweighting matrix is distinct from the summation matrix; and generatereconciled values associated with the particular time point based on thereconciliation matrix, the summation matrix, and the dataset.
 28. Thenon-transitory computer-readable medium of claim 24, wherein aprocessing thread of the computing node is further configured to:determine that the dataset includes a data point with a missing value,the data point being part of a forecast of the plurality of forecasts;and based on determine that the dataset has the data point with themissing value: adjust the summation matrix by removing a column and arow associated with the data point from the summation matrix, to therebycreate an adjusted summation matrix; generate a reconciliation matrixbased on the adjusted summation matrix and a weighting matrix; andgenerate reconciled values associated with the particular time pointbased on the reconciliation matrix and the dataset.
 29. Thenon-transitory computer-readable medium of claim 24, further comprisingprogram code that is executable by the one or more processors forcausing the one or more processors to: receive a plurality of datatables, each data table of the plurality of data tables corresponding toa respective level of the hierarchy and including all data points fromall forecasts of the plurality of forecasts that correspond to thatrespective level of the hierarchy, wherein each row of each data tableof the plurality of data tables corresponds to a single data point of asingle forecast of the plurality of forecasts; and generate a dataset,of the plurality of datasets, by extracting a set of datapointscorresponding to the same time point from the plurality of data tablesand grouping them together as the dataset.
 30. The non-transitorycomputer-readable medium of claim 23, wherein the reconciliation processis configured to determine the plurality of reconciled values by:multiplying a summation matrix by a reconciliation matrix to produce aproduct; and multiplying the product by the plurality of forecasts.